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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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使用机器学习和可解释的人工智能在转录组分析中将时间纳入第三维.

Zubaida Said Ameen1, Auwalu Saleh Mubarak1, Mohamed Hamad2

  • 1Operational Research Center in Healthcare, Near East University, Mersin 99138, Turkey.

Computational biology and chemistry
|March 25, 2025
PubMed
概括
此摘要是机器生成的。

二维转录组分析 (2DTA) 提供了基因表达的快照. 将时间纳入机器学习的第三维度揭示了显著的基因表达模式,改善了数据解释.

关键词:
并且XGBoost也是如此.决策树 (DT) 是指一个决策树.随机森林 (RF) 是一个随机的森林.SHAP可解释的人工智能两个维的转录学 (2DTA)

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 转录组数据分析,或2DTA,在一个时间点测量RNA丰度.
  • 虽然有价值,2DTA有局限性,包括技术变化和缺乏时间数据.
  • 这种"暂时性"对转录基因数据解释的影响尚不清楚.

研究的目的:

  • 调查时间对转录学数据解释的影响.
  • 评估机器学习 (ML) 和可解释AI (XAI) 在解决转录学中的时间性问题的实用性.

主要方法:

  • 在12小时和48小时的时间点利用了25个公开可用的MCF-7细胞的转录组数据集.
  • 应用了三个ML分类器:决策树 (DT),随机森林 (RF) 和XGBoost.
  • 采用沙普利增量解释 (SHAP) 来解释模型的可解释性和使用MSE,MAE和R平方 (DC) 评估性能.

主要成果:

  • 在12小时和48小时数据集之间观察到基因表达模式的显著差异.
  • XGBoost在DT和RF上表现出优越的性能,实现了0.00028的MSE,0.00028的MAE和0.95778.8的R平方.
  • SHAP分析为ML模型的决策过程提供了洞察力.

结论:

  • 时间显著影响转录组数据的解释,这表明2DTA中的"时间性"问题.
  • 机器学习和可解释的人工智能是解决转录学中的时间性问题的有效工具.
  • 这项研究强调了将时间信息整合到转录组分析中的潜力,以获得更强大的生物学见解.